Eradication of human malaria using genetically modified mosquitoes requires the development of mosquito release strategies that will result in successful spread of the transgenic factors, and choosing optimal strategies depends critically on knowledge of genetic exchange among mosquito populations. Assessing gene-flow in Anopheles malaria mosquitoes has been hampered by a lack of data and statistical tools appropriate for the system. The research proposed here aims to develop novel computational methods to detect genetic exchange at different times among populations of Anopheles mosquito vectors of human malaria in sub-Saharan Africa. Current statistical methods are not appropriate for Anopheles, do not distinguish between old and new genetic exchange, and cannot be applied to next-generation sequencing technology data, all of which are necessary to obtain useful estimates of recent gene-flow in Anopheles. A novel method will be developed to incorporate these features, and will be applied to data from three divergent populations of Anopheles. The results of this research will provide valuable estimates of the rate, direction and timing of genetic exchange among three divergent populations, information that is essential for developing optimal malaria eradication strategies.